ChatGPT vs. Copilot vs. Claude — and the answer none of them expected
I built the same Watch Me / Try Me simulation in all three tools. I picked a winner. Then ChatGPT talked me out of using any of them — and into building my own app instead.
Three tools, one identical task
Same 6-step process, same seven screenshots, same audio. Here's how ChatGPT, Copilot and Claude actually compared once all the results were in.
| ChatGPT | Copilot | Claude | |
|---|---|---|---|
| Speed to first build | Fastest (~49 sec) | Slowest to start | In between |
| Asked questions first | No — built immediately | Yes | Yes |
| Hotspot alignment | Needed fixing | Best of the three | Needed fixing |
| Polish by default | Only what you ask for | Partial landing screen | Full landing page + player layer |
| Audio handling | Smooth | Blocked on our license | Smooth |
| Design-as-you-go | Package, then test | Package, then test | Live preview while prompting |
| Output (HTML + SCORM) | Yes | Yes | Yes |
| What slowed me down | Reproducibility | Manual audio workaround | Credit limits + an outage |
All three produced a working, testable simulation and a properly wrapped SCORM file. The differences were in how I got there.
I landed on ChatGPT
Testing all three was genuinely fun, and I learned an enormous amount by doing it. By the time the results were in, I'd decided to stick with ChatGPT. It's enterprise-approved, and as long as I tell it exactly what I want to see, it can do everything I need.
That should have been the end of the story. It wasn't.
ChatGPT suggested I stop prompting entirely
I was going back and forth with ChatGPT about reusability and standardization — my two nagging worries about every AI-built approach. Instead of another prompt or a custom GPT, it suggested something different: write a React Native Web app to do everything I wanted.
That would solve the whole problem. I could define the output once, drop in my screenshots, and package it up — and I could make it look and feel like the Claude version I'd loved so much.
"I was asking about reusability and standardization when ChatGPT suggested that instead of prompting — or even writing a GPT — I write a React Native Web app to do everything I wanted."
The simulation builder I ended up with
My own app: drop in screenshots, place hotspots exactly where I want them, control the pacing, and export the same wrapper every single time.
A walkthrough of my React Native Web simulation builder.
ChatGPT wrote the seed code — Claude grew it
ChatGPT even wrote the initial code for me to hand to Claude. So I imported it and got to prompting. My usage limits were still a factor, so this took a few days — but at the end I had my own web app: drop in screenshots, adjust exactly where each hotspot goes, and control the pacing however I like.
Both tools, doing what each does best: ChatGPT to scaffold, Claude's live design canvas to shape and refine.
Reusable, standardized, and mine
This solved the exact problems prompting never could:
Identical output, every time
The same wrapper and structure on every build — the reproducibility none of the three AIs could guarantee.
No dependency on any one AI
I don't go back to Claude to finish a build, and I don't lose my workflow if a tool gets shut off.
Save, share, and resume
I built in export and re-import, so I can hand working files to a colleague, stop and pick back up, and edit later when an update is needed.
Extensible on demand
When I need a new feature I go back to Claude, add it, and re-export. Branching selections are already on the roadmap.
In testing — and already growing
I'm in testing mode with the new app. A colleague is using it and giving me feedback; from there I'll reiterate and refine until it does exactly what we want, and verify that every simulation is reproducible the way we need.
By far, this was the best outcome of the entire week-long trial. The takeaway? The best tool wasn't a tool at all — it was knowing what great looked like across all three, then building something that delivered it on my terms.
This is how I work with AI
Hands-on experiments, honest comparisons, and a bias toward solutions my team can actually own and reuse.